Minerals from Mines to Mountaintops from Earth to Mars and BeyondPreface
Bibliographic record
Abstract
Other| July 29, 2023 Minerals from Mines to Mountaintops from Earth to Mars and BeyondPreface Roberta L. Flemming; Roberta L. Flemming Search for other works by this author on: GSW Google Scholar Lee A. Groat; Lee A. Groat Search for other works by this author on: GSW Google Scholar Bryan C. Chakoumakos; Bryan C. Chakoumakos Search for other works by this author on: GSW Google Scholar Heather E. Jamieson Heather E. Jamieson Search for other works by this author on: GSW Google Scholar Author and Article Information Roberta L. Flemming Lee A. Groat Bryan C. Chakoumakos Heather E. Jamieson Publisher: Mineralogical Association of Canada Received: 31 May 2023 Accepted: 31 May 2023 First Online: 29 Jul 2023 The Canadian Journal of Mineralogy and Petrology (2023) 61 (4): 651–652. https://doi.org/10.3749/INT014 Article history Received: 31 May 2023 Accepted: 31 May 2023 First Online: 29 Jul 2023 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Roberta L. Flemming, Lee A. Groat, Bryan C. Chakoumakos, Heather E. Jamieson; Minerals from Mines to Mountaintops from Earth to Mars and BeyondPreface. The Canadian Journal of Mineralogy and Petrology 2023;; 61 (4): 651–652. doi: https://doi.org/10.3749/INT014 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyThe Canadian Journal of Mineralogy and Petrology Search Advanced Search Ronald C. Peterson completed his Ph.D. in Geology with special emphasis in mineralogy at Virginia Tech in 1980. At that time, the Department of Geology had an exceptional group of mineralogy, crystallography, and petrology professors, including Donald Bloss, Gerry Gibbs, Paul Ribbe, James Craig, Charles Gilbert, David Wones, and others. Professor Gibbs worked closely with Professor Monte Boisen (Department of Mathematics) to develop and deliver an altogether new way of teaching mineralogy that was truly enlightening, making clear all of crystallography through an elegant and simple mathematical approach. This was so empowering that all the mineralogy and crystallography minded graduate... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.713 | 0.507 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".